There is considerable interest in developing sensors that act as analogs of the mammalian olfactory system (1-2). This system is thought to utilize probabilistic repertoires of many different receptors to recognize a single odorant (3-4). In such a configuration, the burden of recognition is not on highly specific receptors, as in the traditional "lock-and-key" molecular recognition approach to chemical sensing, but lies instead on the distributed pattern processing of the olfactory bulb and the brain (5-6).
Prior attempts to produce a broadly responsive sensor array have exploited heated metal oxide thin film resistors (7-9), polymer sorption layers on the surfaces of acoustic wave resonators (10-11), arrays of electrochemical detectors (12-14), or conductive polymers (15-16). Arrays of metal oxide thin film resistors, typically based on SnO.sub.2 films that have been coated with various catalysts, yield distinct, diagnostic responses for several vapors (7-9). However, due to the lack of understanding of catalyst function, SnO.sub.2 arrays do not allow deliberate chemical control of the response of elements in the arrays nor reproducibility of response from array to array. Surface acoustic wave resonators are extremely sensitive to both mass and acoustic impedance changes of the coatings in array elements, but the signal transduction mechanism involves somewhat complicated electronics, requiring frequency measurement to 1 Hz while sustaining a 100 MHz Rayleigh wave in the crystal (10-11). Attempts have been made to construct sensors with conducting polymer elements that have been grown electrochemically through nominally identical polymer films and coatings (15-18).
It is an object herein to provide a broadly responsive analyte detection sensor array based on a variety of "chemisresistor" elements. Such elements are simply prepared and are readily modified chemically to respond to a broad range of analytes. In addition, these sensors yield a rapid, low power, dc electrical signal in response to the fluid of interest, and their signals are readily integrated with software or hardware-based neural networks for purposes of analyte identification.